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Understanding Dynamic Cross-OSN Associations for Cold-Start Recommendation

文献类型：期刊论文

作者Sang, Jitao1,2; Yan, Ming3; Xu, Changsheng4
刊名IEEE TRANSACTIONS ON MULTIMEDIA
出版日期2018-12-01
卷号20期号:12页码:3439-3451
关键词Cross-OSN association dynamic user modeling cold-start recommendation
ISSN号1520-9210
DOI10.1109/TMM.2018.2839530
通讯作者Sang, Jitao(jtsang@bjtu.edu.cn)
英文摘要Online social networks (OSNs) have become an essential part of people's daily life, and an increasing number of users are now using multiple OSNs for different social media services simultaneously. As a result, user's interests and preferences usually distribute in different OSNs. While most of the existing work mainly aggregates the distributed user behaviors or features directly, recently very few efforts have been focused on understanding the crass-OSN association from collective user behaviors. In this paper, we go one step further to consider the dynamic characteristic of user behaviors and propose a dynamic cross-OSN association mining framework. In this framework, dynamic user modeling is first conducted to capture the drift of user interest in each OSN. A session-based factorization method is then proposed to establish the cross-OSN association in a dynamic manner, by incrementally updating the derived association each time a new session of data arrives. Based on the derived dynamic association, we finally design a cold-start YouTube video recommendation application, by only utilizing users' behaviors in Twitter. Experiments are conducted using real-world user data from Twitter and YouTube. The results demonstrate the effectiveness of this proposed framework in capturing the underlying association between different OSNs and achieving superior cold-start recommendation performance.
WOS关键词SYSTEMS
资助项目National Natural Science Foundation of China[61432019] ; National Natural Science Foundation of China[61720106006] ; National Natural Science Foundation of China[U1705262] ; National Natural Science Foundation of China[61632004] ; National Natural Science Foundation of China[61632115] ; National Natural Science Foundation of China[61672518] ; National Natural Science Foundation of China[61473030] ; National Natural Science Foundation of China[61332016] ; Key Research Program of Frontier Sciences, CAS[QYZDJ-SSW-JSC039] ; Beijing Municipal Science and Technology Commission[Z181100008918012]
WOS研究方向Computer Science ; Telecommunications
语种英语
WOS记录号WOS:000450212600021
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
资助机构National Natural Science Foundation of China ; Key Research Program of Frontier Sciences, CAS ; Beijing Municipal Science and Technology Commission
源URL[http://ir.ia.ac.cn/handle/173211/22614]  
专题自动化研究所_模式识别国家重点实验室_多媒体计算与图形学团队
通讯作者Sang, Jitao
作者单位1.Beijing Jiaotong Univ, Sch Comp & Informat Technol, Beijing 100044, Peoples R China
2.Beijing Jiaotong Univ, Beijing Key Lab Traff Data Anal & Min, Beijing 100044, Peoples R China
3.Alibaba, Hangzhou 311121, Zhejiang, Peoples R China
4.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714		 Sang, Jitao,Yan, Ming,Xu, Changsheng. Understanding Dynamic Cross-OSN Associations for Cold-Start Recommendation[J]. IEEE TRANSACTIONS ON MULTIMEDIA,2018,20(12):3439-3451.
APA Sang, Jitao,Yan, Ming,&Xu, Changsheng.(2018).Understanding Dynamic Cross-OSN Associations for Cold-Start Recommendation.IEEE TRANSACTIONS ON MULTIMEDIA,20(12),3439-3451.
MLA Sang, Jitao,et al."Understanding Dynamic Cross-OSN Associations for Cold-Start Recommendation".IEEE TRANSACTIONS ON MULTIMEDIA 20.12(2018):3439-3451.

入库方式： OAI收割

来源：自动化研究所

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